Artificial neural networks based controller for glucose monitoring during clamp test

Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose...

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Veröffentlicht in:PloS one 2012-08, Vol.7 (8), p.e44587-e44587
Hauptverfasser: Catalogna, Merav, Cohen, Eyal, Fishman, Sigal, Halpern, Zamir, Nevo, Uri, Ben-Jacob, Eshel
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Fishman, Sigal
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Ben-Jacob, Eshel
description Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.
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The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. 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subjects Algorithms
Analysis
Animal models
Animals
Artificial neural networks
Back propagation
Back propagation networks
Biology
Biomedical engineering
Blood
Blood glucose
Blood Glucose - analysis
Blood glucose test
Blood levels
Catheters
Computer Science
Control systems
Control theory
Diabetes
Diagnosis
Engineering
Feedback
Feedback control
Gastroenterology
Gene expression
Genetic algorithms
Glucose
Glucose Clamp Technique - instrumentation
Glucose monitoring
Health problems
Hepatology
Hyperinsulinism - blood
Insulin
Insulin resistance
Kinetics
Machine learning
Medical research
Medicine
Metabolism
Methods
Monitoring, Physiologic - instrumentation
Neural networks
Neural Networks, Computer
Optimization
Plasma
Rats
Rats, Inbred F344
Rats, Sprague-Dawley
Real time
Regression Analysis
Sampling
Stochasticity
Topology
Topology optimization
title Artificial neural networks based controller for glucose monitoring during clamp test
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